A deep learning model to predict dose–volume histograms of organs at risk in radiotherapy treatment plans-Reference-Cited by-同舟云学术

A deep learning model to predict dose–volume histograms of organs at risk in radiotherapy treatment plans

Published:2020-10-15 Issue:11 Volume:47 Page:5467-5481
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Liu Zhiqiang¹,Chen Xinyuan¹,Men Kuo¹,Yi Junlin¹,Dai Jianrong¹

Affiliation:

1. National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College No. 17 Panjiayuannanli, Chaoyang District Beijing100021 China

Funder

Natural Science Foundation of Beijing Municipality

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.14394

Reference45 articles.

1. Optimization of stationary and moving beam radiation therapy techniques

2. Intensity-modulated arc therapy with dynamic multileaf collimation: an alternative to tomotherapy

3. Tomotherapy: A new concept for the delivery of dynamic conformal radiotherapy

4. Patient geometry-driven information retrieval for IMRT treatment plan quality control

5. Quantifying Unnecessary Normal Tissue Complication Risks due to Suboptimal Planning: A Secondary Study of RTOG 0126

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